diff --git a/README.md b/README.md index af33b3f82..ac44e8d6f 100644 --- a/README.md +++ b/README.md @@ -42,8 +42,7 @@ English | [简体中文](README_zh-CN.md) ## Introduction -MMYOLO is an open source toolbox for YOLO series algorithms based on PyTorch. It is -a part of the [OpenMMLab](https://openmmlab.com/) project. +MMYOLO is an open source toolbox for YOLO series algorithms based on PyTorch and [MMDetection](https://github.com/open-mmlab/mmdetection). It is a part of the [OpenMMLab](https://openmmlab.com/) project. The master branch works with **PyTorch 1.6+**. @@ -53,20 +52,20 @@ The master branch works with **PyTorch 1.6+**.
Major features -- **Fair and convenient algorithm evaluation** +- **Unified and convenient benchmark** - MMYOLO unifies the modules of various YOLO algorithms and provides a unified benchmark process. Users can compare and analyze in a fair and convenient way. + MMYOLO unifies the implementation of modules in various YOLO algorithms and provides a unified benchmark. Users can compare and analyze in a fair and convenient way. -- **Detailed introductory and advanced documentation** +- **Rich and detailed documentation** - MMYOLO provides a series of documents from getting started, to model deployment, advanced guidelines, and algorithm analysis, making it easy for different users to get started and make extensions quickly. + MMYOLO provides rich documentation for getting started, model deployment, advanced usages, and algorithm analysis, making it easy for users at different levels to get started and make extensions quickly. - **Modular Design** - MMYOLO decompose the framework into different components and users can easily construct a customized model by combining different modules and training and testing strategies. + MMYOLO decomposes the framework into different components where users can easily customize a model by combining different modules with various training and testing strategies. BaseModule - The picture is provided by RangeKing@GitHub, thank you very much! + The figure is contributed by RangeKing@GitHub, thank you very much!
@@ -75,9 +74,9 @@ The master branch works with **PyTorch 1.6+**. **v0.1.0** was released on 21/9/2022: - Unified component interfaces based on [OpenMMLab 2.0](https://github.com/open-mmlab) and [MMDetection 3.0](https://github.com/open-mmlab/mmdetection/tree/3.x) -- Support for YOLOv5/YOLOX training and deployment, support for YOLOv6 inference and deployment -- Refactored YOLOX for MMDetection to provide faster training and inference -- Detailed introductory and advanced tutorials are provided, see the [English tutorial](https://mmyolo.readthedocs.io/en/latest) +- Support YOLOv5/YOLOX training, support YOLOv6 inference. Deployment will be supported soon. +- Refactored YOLOX from MMDetection to accelerate training and inference. +- Detailed introduction and advanced tutorials are provided, see the [English tutorial](https://mmyolo.readthedocs.io/en/latest). For release history and update details, please refer to [changelog](https://mmyolo.readthedocs.io/en/latest/notes/changelog.html). @@ -101,11 +100,11 @@ mim install -e . ## Tutorial -MMYOLO is based on the MMDetection and uses the same code organization and design approach. To get better use of this, please read [MMDetection Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the first understanding of MMDetection. +MMYOLO is based on MMDetection and adopts the same code structure and design approach. To get better use of this, please read [MMDetection Overview](https://mmdetection.readthedocs.io/en/latest/get_started.html) for the first understanding of MMDetection. -MMYOLO usage is almost identical to MMDetection and all tutorials are straightforward to use, you can also learn about [MMDetection User Guide and Advanced Guide](https://mmdetection.readthedocs.io/en/3.x/). +The usage of MMYOLO is almost identical to MMDetection and all tutorials are straightforward to use, you can also learn about [MMDetection User Guide and Advanced Guide](https://mmdetection.readthedocs.io/en/3.x/). -For different sections than MMDetection, we have also prepared user guides and advanced guides, please read our [documentation](https://mmyolo.readthedocs.io/zenh_CN/latest/). +For different parts from MMDetection, we have also prepared user guides and advanced guides, please read our [documentation](https://mmyolo.readthedocs.io/zenh_CN/latest/). - User Guides diff --git a/README_zh-CN.md b/README_zh-CN.md index 2ecedec36..f253832fb 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -42,20 +42,19 @@ ## 简介 -MMYOLO 是一个基于 PyTorch 的 YOLO 系列算法开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。 +MMYOLO 是一个基于 PyTorch 和 MMDetection 的 YOLO 系列算法开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。 主分支代码目前支持 PyTorch 1.6 以上的版本。 -
主要特性 -- **公平便捷的算法评测** +- **统一便捷的算法评测** - MMYOLO 统一各类 YOLO 算法模块, 并提供统一评测流程，用户可以公平便捷的进行对比分析。 + MMYOLO 统一了各类 YOLO 算法模块的实现, 并提供了统一的评测流程，用户可以公平便捷地进行对比分析。 - **丰富的入门和进阶文档** @@ -75,9 +74,9 @@ MMYOLO 是一个基于 PyTorch 的 YOLO 系列算法开源工具箱。它是 [Op **v0.1.0** 版本已经在 2022.9.21 发布： - 基于 [OpenMMLab 2.0](https://github.com/open-mmlab) 和 [MMDetection 3.0](https://github.com/open-mmlab/mmdetection/tree/3.x) 统一了各组件接口。 -- 支持 YOLOv5/YOLOX 训练和部署，支持 YOLOv6 推理和部署 -- 重构了 MMDetection 的 YOLOX，提供了更快的训练和推理速度 -- 提供了详细入门和进阶教程，详见 [中文教程](https://mmyolo.readthedocs.io/zh_CN/latest) +- 支持 YOLOv5/YOLOX 训练，支持 YOLOv6 推理。即将支持部署。 +- 重构了 MMDetection 的 YOLOX，提供了更快的训练和推理速度。 +- 提供了详细入门和进阶教程，详见 [中文教程](https://mmyolo.readthedocs.io/zh_CN/latest)。 发布历史和更新细节请参考 [更新日志](https://mmyolo.readthedocs.io/zh_CN/latest/notes/changelog.html) @@ -101,11 +100,11 @@ mim install -e . ## 教程 -MMYOLO 基于 MMDetection 开源库，并且采用相同的代码组织和设计方式。为了更好的使用本开源库，请先阅读 [MMDetection 概述](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html) 对 MMDetection 进行初步的了解。 +MMYOLO 基于 MMDetection 开源库，并且采用相同的代码组织和设计方式。为了更好的使用本开源库，请先阅读 [MMDetection 概述](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html) 对 MMDetection 进行初步地了解。 MMYOLO 用法和 MMDetection 几乎一致，所有教程都是通用的，你也可以了解 [MMDetection 用户指南和进阶指南](https://mmdetection.readthedocs.io/zh_CN/3.x/) 。 -针对和 MMDetection 不同部分，我们也准备了用户指南和进阶指南，请阅读我们的 [文档](https://mmyolo.readthedocs.io/zh_CN/latest/) 。 +针对和 MMDetection 不同的部分，我们也准备了用户指南和进阶指南，请阅读我们的 [文档](https://mmyolo.readthedocs.io/zh_CN/latest/) 。 - 用户指南 diff --git a/docs/en/notes/changelog.md b/docs/en/notes/changelog.md index 61d23f930..2cf98bb9d 100644 --- a/docs/en/notes/changelog.md +++ b/docs/en/notes/changelog.md @@ -6,6 +6,6 @@ We have released MMYOLO open source library, which is based on MMEngine, MMCV 2. ### Highlights -1. Support for YOLOv5/YOLOX training and deployment, support for YOLOv6 inference and deployment. -2. Refactored YOLOX for MMDetection to provide faster training and inference. -3. Detailed introductory and advanced tutorials are provided, see the [English tutorial](https://mmyolo.readthedocs.io/en/latest). +1. Support YOLOv5/YOLOX training, support YOLOv6 inference. Deployment will be supported soon. +2. Refactored YOLOX from MMDetection to accelerate training and inference. +3. Detailed introduction and advanced tutorials are provided, see the [English tutorial](https://mmyolo.readthedocs.io/en/latest). diff --git a/docs/zh_cn/notes/changelog.md b/docs/zh_cn/notes/changelog.md index 5e14e0e9a..b57bebd69 100644 --- a/docs/zh_cn/notes/changelog.md +++ b/docs/zh_cn/notes/changelog.md @@ -6,6 +6,6 @@ ### 亮点 -1. 支持 YOLOv5/YOLOX 训练和部署，支持 YOLOv6 推理和部署 -2. 重构了 MMDetection 的 YOLOX，提供了更快的训练和推理速度 -3. 提供了详细入门和进阶教程, 包括 YOLOv5 从入门到部署、YOLOv5 算法原理和实现全解析、 特征图可视化等教程 +1. 支持 YOLOv5/YOLOX 训练，支持 YOLOv6 推理。部署即将支持。 +2. 重构了 MMDetection 的 YOLOX，提供了更快的训练和推理速度。 +3. 提供了详细入门和进阶教程, 包括 YOLOv5 从入门到部署、YOLOv5 算法原理和实现全解析、 特征图可视化等教程。